Project Abstract Overview: The parent project for this supplement aims to provide routine MRI of subjects with total joint replacements by reducing the severe image artifacts near metal, while offering highly efficient patient-specific scans that can detect bone loss, infection, and temperature changes near the implant in clinically feasible scan times. The supplement aims to incorporate deep learning techniques to better meet the parent grant goals. Relevance: Total joint replacements are one of the most successful orthopedic procedures, used annually to reduce pain from joint diseases in about one million patients in the United States (a number projected to double by 2030). However, about 10% of joint replacements fail in 5-10 years due to bone loss (osteolysis), infection, or other complications, often leading to revision surgery. Accurate, early, non-invasive assessment of complications remains limited, but would offer earlier and less invasive treatments, reduce unnecessary surgery, or allow better surgical planning. Approach: Prior to, and during the parent grant period, we have developed novel ?multi-spectral imaging? (MSI) MRI techniques that allow visualization of pathology adjacent to metallic implants, and together with other groups have successfully applied them to imaging of patients with devices including joint replacements and spinal fixation hardware. However these methods remain slow, have limited spatial resolution, and are challenging to use routinely. The recent growth of the machine learning field including convolutional neural networks (CNNs), and its application to medical imaging offers unique opportunities to substantially improve MRI near metal, and specifically the goals of the parent grant. We propose 3 small, independent aims in the supplement: (1) to bring fast, isotropic imaging near metal to clinical practice by using CNN-based methods to reduce reconstruction times to under 30 seconds, (2) to improve image quality away from metal by using a new reconstruction and CNN to avoid needing standard imaging in addition to MSI methods and (3) to reduce background-gradient induced artifacts near to metal using a CNN-based approach to enable better diagnosis of abnormalities adjacent to metal. Summary: We aim to supplement our parent grant with CNN-based approaches to speed up scanning and image reconstruction, and to improve image quality near to and way from metal. These techniques will allow routine, non-invasive evaluation for earlier and more accurate detection and treatment of complications in these patients, as well as numerous other applications of MRI near metal implants.